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Differential evolution and analytic programming in the case of trigonometric identities discovery
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| dc.title | Differential evolution and analytic programming in the case of trigonometric identities discovery | en |
| dc.contributor.author | Komínková Oplatková, Zuzana | |
| dc.contributor.author | Šenkeřík, Roman | |
| dc.contributor.author | Viktorin, Adam | |
| dc.relation.ispartof | 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP) | |
| dc.identifier.issn | 2157-8672 Scopus Sources, Sherpa/RoMEO, JCR | |
| dc.identifier.isbn | 978-1-5386-6979-2 | |
| dc.date.issued | 2018 | |
| utb.relation.volume | 2018-June | |
| dc.event.title | 25th International Conference on Systems, Signals and Image Processing, IWSSIP 2018 | |
| dc.event.location | Maribor | |
| utb.event.state-en | Slovenia | |
| utb.event.state-cs | Slovinsko | |
| dc.event.sdate | 2018-06-20 | |
| dc.event.edate | 2018-06-22 | |
| dc.type | conferenceObject | |
| dc.language.iso | en | |
| dc.publisher | IEEE Computer Society | |
| dc.identifier.doi | 10.1109/IWSSIP.2018.8439705 | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/8439705 | |
| dc.subject | Analytic Programming | en |
| dc.subject | Differential Evolution | en |
| dc.subject | Trigonometric identities | en |
| dc.description.abstract | The paper deals with the discovery of trigonometric identities of four functions via Analytic Programming and four strategies of Differential evolution (canonical DE/Rand/l/Bin, chaos-based DE/Rand/l/Bin-Lozi, DE/Rand/l/Bin-Burgers and SHADE). The results showed that all four strategies were comparable for this specific task. © 2018 IEEE. | en |
| utb.faculty | Faculty of Applied Informatics | |
| dc.identifier.uri | http://hdl.handle.net/10563/1008212 | |
| utb.identifier.obdid | 43879081 | |
| utb.identifier.scopus | 2-s2.0-85053146930 | |
| utb.identifier.wok | 000451277200061 | |
| utb.source | d-scopus | |
| dc.date.accessioned | 2018-10-03T11:13:03Z | |
| dc.date.available | 2018-10-03T11:13:03Z | |
| dc.description.sponsorship | Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [Action CA15140]; Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO); High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) [Action IC1406]; A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin | |
| utb.ou | CEBIA-Tech | |
| utb.contributor.internalauthor | Komínková Oplatková, Zuzana | |
| utb.contributor.internalauthor | Šenkeřík, Roman | |
| utb.contributor.internalauthor | Viktorin, Adam | |
| utb.fulltext.affiliation | Zuzana Kominkova Oplatkova 1, Roman Senkerik 2, Adam Viktorin 3 1,2,3 Tomas Bata University in Zlin, Faculty of Applied Informatics, Nam. T.G.Masaryka 5555, Zlin, Czech Republic oplatkova@utb.cz | |
| utb.fulltext.dates | - | |
| utb.fulltext.references | [1] S. M. Musa, “Chapter 7 – Trigonometry”, In Fundamentals of Technical Mathematics, Academic Press, 2016, p. 187-219, ISBN 9780128019870, https://doi.org/10.1016/B978-0-12-801987-0.00007-1. [2] J. R. Koza, “Hierarchical genetic algorithms operating on populations of computer programs”, In Proceedings of the 11th International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann. Volume I. pp. 768-774, 1989. [3] C. Ryan, M. O'Neill, J.J. Collins, “Grammatical evolution: Solving trigonometric identities” Proceedings of Mendel 1998: 4th International Mendel Conference on Genetic Algorithms, Optimisation Problems, Fuzzy Logic, Neural Networks, Rough Sets, pp. 111-119, (1998). [4] Hoai N.X. “Solving Trigonometric Identities with Tree Adjunct Grammar Guided Genetic Programming”, In: Abraham A., Köppen M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg, p. 339-351 2002, ISBN 978-3-7908-1480-4 doi. https://doi.org/10.1007/978-3-7908-1782-9_25 [5] I. Zelinka, D. Davendra, R. Senkerik, R. Jasek and Z. Oplatkova “Analytical Programming - a Novel Approach for Evolutionary Synthesis of Symbolic Structures”, in Kita E.: Evolutionary Algorithms, InTech 2011, ISBN: 978-953-307-171-8 [6] Z. Oplatkova, “Metaevolution: Synthesis of Optimization Algorithms by means of Symbolic Regression and Evolutionary Algorithms”, Lambert Academic Publishing Saarbrücken, 2009, ISBN: 978-3-8383-1808-0 [7] F. Olivetti de França, “A greedy search tree heuristic for symbolic regression”, Information Sciences, Volumes 442–443, 2018, pp 18-32, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2018.02.040. [8] Storn R.and Price K., “Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, pp. 341–359, 1997. [9] K. V. Price, R. M. Storn, and J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization, ser. Natural Computing Series. Berlin, Germany: Springer-Verlag, 2005. [10] F. Neri and V. Tirronen, “Recent Advances in Differential Evolution: A Survey and Experimental Analysis,” Artificial Intelligence Review, vol. 33, no. 1–2, pp. 61–106, 2010. [11] S. Das and P. N. Suganthan, “Differential Evolution: A Survey of the State-of-the-art,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2011. [12] S.Das, S.S.Mullick, and P.Suganthan, “Recent advances in differential evolution – An updated survey,” Swarm and Evolutionary Computation, vol. 27, pp. 1–30, 2016. [13] J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer, “Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 646–657, 2006. [14] A. K. Qin, V. L. Huang, and P. N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 98–417, 2009. [15] J.Brest,P.Korosec,J.Silc,A.Zamuda,B.Boskovic and M.S.Maucec, “Differential evolution and differential ant-stigmergy on dynamic optimisation problems,” International Journal of Systems Science, vol. 44, no. 4, pp. 663–679, 2013. [16] R. Tanabe and A. S. Fukunaga, “Improving the search performance of SHADE using linear population size reduction,” in 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014, pp. 1658–1665. [17] R. Senkerik, M. Pluhacek, I. Zelinka, Z. Oplatkova, R. Vala, and R. Jasek, "Performance of Chaos Driven Differential Evolution on ShiftedBenchmark Functions Set," in International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. vol. 239, Á. Herrero, B. Baruque, F. Klett, A. Abraham, V. Snášel, A. C. P. L. F. Carvalho, et al., Eds., ed: Springer International Publishing, 2014, pp. 41-50. [18] R. Senkerik, D. Davendra, I. Zelinka, M. Pluhacek, and Z. Kominkova Oplatkova, "On the Differential Evolution Driven by Selected Discrete Chaotic Systems: Extended Study," in 19th International Conference on Soft Computing, MENDEL 2013, 2013, pp. 137-144. [19] Z. Kominkova Oplatkova, A. Viktorin, R. Senkerik. “Comparison of Three Novelty Approaches to Constants (Ks) Handling in Analytic Programming Powered by SHADE”, Mendel, Springer Series 2018, unpublished [20] T. Urbanek, Z. Prokopova, R. Silhavy, A. Kuncar, “New Approach of Constant Resolving of Analytical Programming”, In 30th European Conference on Modelling and Simulation, 2016, p. 231-236. ISBN 978-0-9932440-2-5. [21] A. Viktorin, M. Pluhacek, Z. Kominkova Oplatkova, R. Senkerik, “Analytical Programming with Extended Individuals”, In 30th European Conference on Modelling and Simulation, 2016, p. 237-244. ISBN 978-0-9932440-2-5. | |
| utb.fulltext.sponsorship | This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of NatureInspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz). | |
| utb.wos.affiliation | [Oplatkova, Zuzana Kominkova; Senkerik, Roman; Viktorin, Adam] Tomas Bata Univ Zlin, Fac Appl Informat, Nam TG Masaryka 5555, Zlin, Czech Republic | |
| utb.scopus.affiliation | Tomas Bata University in Zlin, Faculty of Applied Informatics, Nam. T.G.Masaryka 5555, Zlin, Czech Republic | |
| utb.fulltext.projects | LO1303 (MSMT7778/2014) | |
| utb.fulltext.projects | CZ.1.05/2.1.00/03.0089 | |
| utb.fulltext.projects | IGA/CebiaTech/2018/003 | |
| utb.fulltext.projects | CA15140 | |
| utb.fulltext.projects | ImAppNIO | |
| utb.fulltext.projects | IC1406 | |
| utb.fulltext.projects | cHiPSet |
